Who is susceptible and why? An agent-based approach to assessing vulnerability to drought

Krömker, Dörthe; Eierdanz, Frank; Stolberg, Andreas

doi:10.1007/s10113-008-0049-5

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…Then the sum of the different consequences was used to define the membership functions. iii) Inference modelling Kro¨mker et al (2008): The core of this approach for quantifying exposure to drought was the construction of an inference model. This model consisted of a rule system made up of linguistic statements, in turn made up of variables described by fuzzy logic (Roberts 1996), Wu et al (1996), Silvert (2000), Mackay and Robinson (2000) and Kangas and Kangas (2004).…”

Section: Data Analysis Methodsmentioning

confidence: 99%

“…Exposure the researcher assessed defenselessness to drought. That included getting the indicators to quantify and measure proneness according to (Acosta-Michlik et al 2008;Kro¨mker et al 2008) and Ta¨nzler and Carius (2008). From the questionnaire the researcher got data to derive membership functions for different categories of the indicator of Negative consequences of drought.…”

Section: Data Collection Proceduresmentioning

confidence: 99%

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Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

2020

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According to IPCC, (2007) Vulnerability is the degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change, including climate variability and extremes. It is a function of exposure, sensitivity, and adaptive capacity (Marshall et al. 2010; Wongbusarakum and Loper 2011, Mucke 2012). The study sought to determine the vulnerability to climate related shocks among smallholder farmers in Kinakomba Ward. The main purpose of the study was to assess the extent to which, exposure, sensitivity and adaptive capacity contributed to the vulnerability of the smallholder farmers on their livelihoods. The study was carried out in Kinakomba ward in Tana River County, Kenya. A descriptive survey research design was used. Stratified random sampling was employed to select 390 out of a population of 3,908 households. Data was collected using questionnaires and Focus Group Discussions. Descriptive statistics, metric of sensitivity, composite index was used to analyse sensitivity. Two methods were used to analyse exposure. Firstly the fuzzy logic in assessing susceptibility to drought involving a selection of input variables, Fuzzification, inference modelling and defuzzification and secondly DrinC Model software. The researcher used two methods to analyse adaptive capacity namely the interview with 390 farming households to gather data on farming and household characteristics and natural resources availability and secondly a panel of 15 Key Informants provided ratings of indicators of adaptive capacity using analytic hierarchy process (AHP). To understand the interaction of these variables this study Analysed a quantitative Vulnerability score by using an equation which combined three contributing indices of exposure, sensitivity and adaptive capacity each normalized to 0-1 Scale (Adger and Vincent, 2005;Allison et al. 2009), the vulnerability score was obtained by adding exposure to sensitivity and subtracting the adaptive capacity. The key results from the study showed that exposure (p=0.000066) and sensitivity (P=0.00038) had a significant effect on livelihoods. These factors were also found to have a negative influence on livelihoods in the area. Further statistical findings showed that as adaptive capacity increased vulnerability decreased while when sensitivity increased vulnerability increased at the same time. The study concluded that these two dimensions of vulnerability could be modified by policy and development. The study also concluded that exposure contributed more than sensitivity and adaptive capacity to the cumulative vulnerability. The study recommended that the County and National governments and stakeholders to employ measures to adapt to climate change and variability. This study also recommended that the Government in partnership with other stakeholders develop a comprehensive disaster risk management framework to address the drought hazards and undertake mitigation and adaptation measures by equipping the smallholder farmers with knowledge on how to cope with the cyclic...

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Section: Data Analysis Methodsmentioning

confidence: 99%

Section: Data Collection Proceduresmentioning

confidence: 99%

Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

2020

FSQM

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“…This was achieved by asking the respondents to appraise eight classes of possible consequences of drought which led to a four step methodology according to (Acosta-Michlik et al 2008;Kro¨mker et al 2008) and Ta¨nzler and Carius, (2008) as follows: i) Selection of input variables, ii) Fuzzification, iii) Inference modelling and iv) Defuzzification (Bothe 1998, Aliev et al 2000 2.7.1 Selection of input variables. The researcher using the data collected on the indicators analyzed the variables according to how different respondents had responded.…”

Section: Measuring Exposure By Fuzzification Methodsmentioning

confidence: 99%

“…To quantify susceptibility an inference model consisting of a rule system made up of linguistic statements of the variables described by the fuzzy logic was constructed according to Kro¨mker et al (2008), (Roberts, 1996), Wu et al (1996, Silvert (2000), Mackay and Robinson (2000) and Kangas and Kangas (2004). This system defines the relationship between a given combination of indicators and a rule is needed for all variables and all their categories.…”

Section: Inference Modellingmentioning

confidence: 99%

Climate Change Shocks Exposure Index to Drought on the Livelihoods of the Smallholder Farmers in Kinakomba Ward, Tana River County, Kenya

2020

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Being susceptible to climate change means being unable to cope with the adverse effects of climate change especially droughts and a likelihood of experiencing harm due to its occurrence. The study sought to evaluate the effects of exposure to Climate related shocks on the livelihoods of the smallholder farmers with the intent of formulating appropriate policies to enable them cope with its impacts. A descriptive survey research design was used. Stratified random sampling was employed to select 390 households. Two methods were used to analyse exposure. Firstly the fuzzy logic in assessing susceptibility to drought involving a selection of input variables, Fuzzification, inference modelling and defuzzification and secondly DrinC Model software. The results revealed that the final value of the negative consequences of drought was 0.35.The study also established a single index as 0.45 for exposure for the entire study period of 35 years for Kinakomba Ward .The study showed that exposure was statistically significant at (0.000066). The study further revealed that the periods between occurrence of extreme droughts were reducing and at the same time that droughts were moving from being severe to being extreme within shorter periods of time leaving smallholder farmers who depend on rain fed agriculture with high exposures and risks as well as experiencing longer hunger periods with severe implications on their food and nutritional security for the vast populations in the study area. The Study concluded that the exposure to drought of the smallholder farmers in Kinakomba Ward is significantly related to their farming livelihood systems. This study recommends that the County Government in partnership with the National Government and other stakeholders develop a comprehensive disaster risk management framework to address the drought hazards and undertake mitigation and adaptation measures by equipping the smallholder farmers with knowledge on how to cope with the cyclic and vicious droughts' impacts that have led to serious irreversible harm to humans and livestock in the area.

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“…Seventeen of the 24 studies used a distinctive underlying theory, of which almost half of the studies strictly followed the chosen theory throughout the study [32]. The rest of the studies loosely referred to a theory, using it mainly as inspiration for their conceptual framework or model [43]. In the analyzed studies, we identify three different types of theories for the conceptualization of human responses, including (1) economicallybased utility maximization theories, (2) behavioral theories from the field of sociology, psychology and migration studies, and (3) theories based on vulnerability and resilience concepts.…”

Section: Theories and Approaches To Conceptualizing Human Responses T...mentioning

confidence: 99%

Quantitative Modeling of Human Responses to Changes in Water Resources Availability: A Review of Methods and Theories

2021

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In rural areas in developing countries where livelihoods directly depend on agriculture, shortage of water can have severe socio-economic and humanitarian consequences and has been suggested to result in conflict and migration. Understanding such responses is important for the development of effective water management policies and other interventions. However, despite the availability of extensive knowledge on water-related human behavior, water resources planning studies do not always look beyond direct impacts. Therefore, this paper assesses literature on water-related human responses, the quantification and conceptualization methods and theories used, the scale at which models are applied, and the extent to which findings are used to make policy recommendations. We found system dynamics approaches mostly applied for policy evaluations, but often with a limited integration of human behavior beyond water use; agent-based models seem to be suited for policy analysis, but only limitedly applied for that purpose; and statistical studies to present the widest range of human responses and explanatory factors, but without making the behavioral mechanisms explicit. In fact, only a limited number of studies was based on behavioral theories. Based on these findings we recommend eight steps to facilitate quantification of human responses for water resources planning purposes.

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Who is susceptible and why? An agent-based approach to assessing vulnerability to drought

Cited by 12 publications

References 15 publications

Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

Climate Change Shocks Exposure Index to Drought on the Livelihoods of the Smallholder Farmers in Kinakomba Ward, Tana River County, Kenya

Quantitative Modeling of Human Responses to Changes in Water Resources Availability: A Review of Methods and Theories

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